A growing research body is concerned with the analysis of the potential use of social media contents, namely VGI and SMGI, for disaster and crisis management. Different methodologies and procedures dealing with SMGI for extracting useful knowledge to support analysis and decision-making are found in literature.
First of all, an application to retrieve disasters’ eyewitness photos from Flickr is exposed. The application proposes a methodology based upon a qualitative study to investigate if and how users’ activity on Flickr may evolve in case of notable disasters (Liu et al., 2008). By means of a qualitative study, the collected images regarding the hazard, the post-impact and the online convergence might be significant to disaster response efforts. In the study the photos of latter kind, especially, combining data from different sources, are found useful to create new overviews and overlapping maps, supplying spatial information on the event location and the potential relief resources. The study proposes an approach to capture SMGI, stressing the potential of this information for disaster response and recovery issues; however the analytical methodology relies upon a manual intervention for the identification and the extraction of useful data in order to implement a qualitative analysis.
Secondly, an application to enhance the situational awareness (SA), through analysis of Twitter posts during a disaster is reviewed (Vieweg et al., 2010). The aim of the study is the harvesting of real-time contents during a crisis event according to lifecycle production and consumption of information in microblogging.
SMGI concerning two disaster events occurred in the 2009 in USA, relying on the Twitter API to perform the collection of data has been analyzed. The information extraction from tweets is based on well-defined terms related to each event, taking advantage of an initial investigation of the Twitter public stream.
Afterwards, the geographic references of each tweet are obtained through a manual analysis of users locations in order to achieve a manageable dataset. The proposed analyses expose differences in behaviors of people between the warning phase, namely anticipatory awareness of a disaster, and the impact phase, or real-time awareness of the event. Moreover, the results exhibit a growing percentage of georeferenced tweets during the impact phase, suggesting the intent of users to supply more useful information to the online community. The application proposes several procedures and analysis for managing SMGI of Twitter, as well as, the use of advanced instruments and technology. Nevertheless, a manual intervention is even required for data collection and analysis.
Thirdly, another study presents an ad-hoc application to detect events related to disasters by Twitter contents. The Twitter Event Detection Analysis System (TEDAS) aims to detect and rank new events
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 48 according to their importance, generating spatial and temporal patterns of the extracted data (Li et Al, 2012). The proposed application relies on Java, PHP and Application Programming Interfaces of Twitter and Google Maps to collect tweets and the related users’ location, according to well-defined terms for research queries. The obtained results offer an overview of spatial and temporal patterns of the detected events in real-time directly. In this case the study takes advantage of more technologically advanced tools, exclusively, which limited manual harvests.
Similarly, another study presents an approach to categorize tweets by analyzing the Twitter broadcasted contents during the Black Saturday brush fires of 2009 in Australia (Sinnappan et al., 2010). Here the authors classified the tweets using a categorization scheme designed for disaster events, specifically, in order to evaluate the percentage of tweets providing potential useful information. The results, based on a sample of 1684 tweets, demonstrate how only 5% of SMGI contained proficient data regarding the disaster with directly actionable information, while the information remained may be classified as noise or not directly actionable.
Along the same vein, a study conducted by Starbird et al. (2010) analyzes data collected from a particular subset of users commenting on the Red River flood of 2009 in the United States and Canada, looking at the particular features of their discussion. The aim is the investigation of the Twitter usage patterns during the disasters, relying on the qualitative analysis of tweets, which contained several disaster-related terms, collected during the event period. The results conduct to the identification of two overlapping categories of useful tweets, namely generative and synthetic. The former category includes tweets that provide new information through the description of lived experiences and facts, while the latter contains tweets that merge a variety of information from external sources and spread this new information. In addition, the results demonstrate how major hurdles arise in finding original tweets, for representing less than 10% of the analyzed sample, as well as, tweets from users directly afflicted by the event that represent less than 2% of the sample.
Zin et al. (2013) introduce an application developed in order to extract visual and textual data from YouTube and Twitter respectively, for describing the situation awareness related to disasters). The study proposes and approach composed by several steps to analyze SMGI, focusing on location, network, contents and time. Statistical operations are carried out on collected data to rank the detected events according to the relative importance; however, the application requires an empirical procedure to a proper data management, leading toward differences in results if the events detection relies on textual SMGI or visual SMGI.
In their study, Spinsanti and Ostermann (2013) present the Geographic CONtext Analysis of Volunteered Geographic Information (GeoCONAVI), a prototype system which aims to retrieve, process, analyze and
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 49 finally evaluate social media related forest fires contents. The goal of the approach is to evaluate the opportunities that VGI and SMGI may offer as trustworthy and actionable information during disaster events. In this approach the authors integrate the SMGI dataset with further official available information concerning the geographic context of the events, so enabling an immediate assessment of quality and reliability. Moreover, the approach builds on a set of spatial-temporal clusters techniques in order to support the scoring and the final validation of retrieved information.
In addition several studies are concerned on the capability to detect such kind of phenomena from the use of social networks in the domain of disaster and emergency management are carried out.. For example Sakaki et al. (2010) use the microblogging platform Twitter for event detection, which benefit from real-time production and consumption of information. A few semantic analyses are applied to Twitter SMGI in order to classify them into positive or negative classes via a support vector machine (SVM) approach. The aim of the study is to develop an earthquake reporting system, able to detect an event based on sensory observations, namely the tweets. In order to identify the earthquake events and establish the correct location, the reporting system relies on probabilistic models and location estimation methods.
Similarly to the previous study, Crooks et al. (2013) proposes an approach to analyze the spatial and temporal dimensions of Twitter contents related to a 5.8 magnitude earthquake, which occurred on August 2011 in the East Coast of the United States. The authors demonstrate how Twitter feeds may represent a hybrid form of a sensor system allowing the identification and localization of the event epicenter. Despite the limited quantity of extractable Twitter SMGI, which is only a 1% sample of the real volume, the study demonstrates how it is possible to detect the event location within 5% of time that is usually required by currently available systems. In addition, interesting spatial-temporal patterns, starting from the impact area firstly and then spreading over time across other locations, were identified from tweets origins. Results demonstrate that an early collection of Twitter SMGI may be used to provide a rapid approximation of the earthquake impact area.
Finally, a study proposed by de Albuquerque et al. (2015) recommends an approach for integrating social media contents with official information in order to deal with disaster events. The suggested approach aims to enhance the identification of relevant messages from Twitter relying upon the intertwined relations between SMGI and the contextual geographic features of the event, which may be derived from authoritative data, such as sensor data, hydrological data and digital elevation models. The study is carried out analyzing Twitter SMGI produced in June 2013 during the River Elbe Flood in Germany and applying statistical analysis for detecting general spatial patterns in the occurrence of event-related SMGI, associated with both proximity and severity of the flood. The study is carried out analyzing Twitter SMGI produced in June 2013 in Germany during the River Elbe's flood and applying statistical analysis in order to
Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 50 detect general spatial patterns in the occurrence of SMGI flood-related and associated with both its proximity and severity. The study demonstrates how SMGI contributed near the afflicted area shows a higher probability of being related to the event, providing reliable and useful information for managing disasters.